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          /sandboxes/homework-5-new-tlhaksa1/
          Name
          ...
          Last Modified
          File Size
          • hw5.ipynb20 minutes ago1.3 MB
          • README.md11 hours ago1.9 KB
          • sankey.html11 hours ago3.5 MB
          • stacked_bars.html12 hours ago3.5 MB
          • README.md
          • hw5.ipynb
          • stacked_bars.html
          • sankey.html
          99
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          [![Review Assignment Due Date](https://classroom.github.com/assets/deadline-readme-button-24ddc0f5d75046c5622901739e7c5dd533143b0c8e959d652212380cedb1ea36.svg)](https://classroom.github.com/a/YXOeu4qD)

          1. Create a python file that webscrapes GDP by country and plots a stacked interactive bar plot using plotly. Stack countries within regions using the IMF numbers. Please include this in your ipython notebook and output your plot to an html file containing the plot.
          2. Look at the chapter on interactive graphics and, specifically, the code to display a subject's MRICloud data as a sunburst plot. Do the following. Display this subject's data as a Sankey diagram. Display as many levels as you can (at least 3) for Type = 1, starting from the intracranial volume.
          3. Create a simple webpage containing the Sankey graphic and host it on github pages. Do not- host this off of your assignment repo from github classroom, since this is not public. Instead, you'll have to create a new public repo from your regular github account and add this file. Put the link to your live web page in a markdown cell of your hw5.ipynb file as a text block.

          Your homework should include
          1. An file called hw5.ipynb that has your code for parts 1 and 2.
          2. Two html files, one called sankey.html and one called stacked_bar.html that contain the two plots as html files.
          3. Your hw3.ipynb file should have a text block that contains a link to the live and publicly hosted sankey diagram.


          Remember that you should have two repositories for this assignment. First, you need the HW repository that you create when you accept the assignment. This should contain the three files (hw5.ipynb, sankey.html, stacked_bar.html). Secondly, you will need to create your own repository containing a live link to your sankey html file. So, when I click on that link it should show a page containing your plot.

          Note plotly objects contain a method called to_html() which is useful for creating an html file.
          Kernel status: Unknown
          # Part 1

          Part 1¶

          [48]:
          import requests as rq
          import bs4
          import pandas as pd
          [50]:
          Selection deleted
          # read webpage into data
          url = 'https://en.wikipedia.org/wiki/List_of_countries_by_GDP_(nominal)'
          gdp = rq.get(url)

          ## print out the first 200 characters to see what it looks like
          gdp.text[0 : 99]
          [50]:
          '<!DOCTYPE html>\n<html class="client-nojs vector-feature-language-in-header-enabled vector-feature-l'
          [51]:
          # read page into bs4
          bs4page = bs4.BeautifulSoup(gdp.text, 'html.parser')
          tables = bs4page.find_all('table',{'class':"wikitable"})
          [52]:
          from io import StringIO

          # Read the table from the StringIO object into pandas
          # Note most recent version of pandas won't accept a string as input, it needs to be passed through stringio
          gdp = pd.read_html(StringIO(str(tables[0])))[0]

          # drop missing data
          gdp = gdp.dropna()

          # rename columns
          gdp = gdp.rename(columns={'Country/Territory': 'Country','UN region' : 'Region','IMF[1][13]': 'IMF', 'World Bank[14]' : 'WorldBank', 'United Nations[15]':'UnitedNations'})

          # Remove "world" data(the first row; index 0)
          gdp = gdp.iloc[1:]

          # Remove and rename header row
          gdp.columns = gdp.iloc[0:8]
          gdp.columns = ['Country', 'Region', 'IMF_Forecast', 'IMF_Year','WoldBank_Estimate','WorldBank_Year','UN_Estimate','UN_Year']

          # display table
          gdp.head()
          [52]:
          Country Region IMF_Forecast IMF_Year WoldBank_Estimate WorldBank_Year UN_Estimate UN_Year
          1 United States Americas 26949643 2023 25462700 2022 23315081 2021
          2 China Asia 17700899 [n 1]2023 17963171 [n 3]2022 17734131 [n 1]2021
          3 Germany Europe 4429838 2023 4072192 2022 4259935 2021
          4 Japan Asia 4230862 2023 4231141 2022 4940878 2021
          5 India Asia 3732224 2023 3385090 2022 3201471 2021
          [53]:
          # Extract only the relevant columns
          imf = gdp[['Country', 'Region', 'IMF_Forecast']]
          [54]:
          # Rename IMF column
          imf = imf.rename(columns={'IMF_Forecast' : 'Forecast'})

          print(imf.head())
                   Country    Region  Forecast
1  United States  Americas  26949643
2          China      Asia  17700899
3        Germany    Europe   4429838
4          Japan      Asia   4230862
5          India      Asia   3732224
          [55]:
          # Check Forecast data type
          print(imf['Forecast'].dtypes) 
          object
          [56]:
          import numpy as np

          # Replace non-numeric values with NaN
          imf['Forecast'] = imf['Forecast'].replace('—', np.nan)

          # Convert 'Forecast' column to numeric data type
          imf['Forecast'] = pd.to_numeric(imf['Forecast'], errors='coerce')

          # Filter rows where 'Forecast' column is non-zero
          imf = imf.loc[imf['Forecast'] != 0]

          # Reset index if needed
          imf.reset_index(drop=True, inplace=True)

          # Show data type of 'Forecast' column
          print(imf['Forecast'].dtypes)
          float64
          [57]:
          # Convert IMF to long (unstack converts to tuple)
          page = 'IMF Forecasted GDP (USD in Millions)'
          y_imf = gdp[gdp['IMF_Forecast'] == page].drop(['IMF_Year','WoldBank_Estimate','WorldBank_Year','UN_Estimate','UN_Year'], axis=1).unstack()

          # Check
          print(imf[0:])
          y = np.asarray([10, 20, 30, 40, 50])
          y = y[1 : y.size] - y[0 : (y.size - 1)]
          print(y)
                     Country    Region    Forecast
0    United States  Americas  26949643.0
1            China      Asia  17700899.0
2          Germany    Europe   4429838.0
3            Japan      Asia   4230862.0
4            India      Asia   3732224.0
..             ...       ...         ...
207          Palau   Oceania       267.0
208       Kiribati   Oceania       246.0
209          Nauru   Oceania       150.0
210     Montserrat  Americas         NaN
211         Tuvalu   Oceania        63.0

[212 rows x 3 columns]
[10 10 10 10]
          [58]:
          import plotly.express as px

          # Plot stacked bar of countries within regions using the IMF numbers
          fig = px.bar(imf, x = "Region", y = "Forecast", color = "Country", title='IMF Forecasted GDP by Country',
          labels={'Forecast': 'GDP (USD Million)', 'Region': 'UN Region', 'Country': 'Country'})

          fig.show()
          /opt/tljh/user/lib/python3.10/site-packages/plotly/express/_core.py:2065: FutureWarning:

When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.
          AmericasAsiaEuropeOceaniaAfrica010M20M30M40M
          CountryUnited StatesChinaGermanyJapanIndiaUnited KingdomFranceItalyBrazilCanadaRussiaMexicoSouth KoreaAustraliaSpainIndonesiaTurkeySaudi ArabiaNetherlandsSwitzerlandPolandTaiwanBelgiumArgentinaSwedenIrelandNorwayAustriaIsraelThailandUnited Arab EmiratesSingaporeBangladeshPhilippinesVietnamMalaysiaDenmarkEgyptNigeriaHong KongSouth AfricaIranColombiaRomaniaChilePakistanCzech RepublicFinlandIraqPortugalPeruKazakhstanNew ZealandGreeceQatarAlgeriaHungaryUkraineKuwaitEthiopiaMoroccoSlovakiaCubaDominican RepublicEcuadorPuerto RicoKenyaOmanBulgariaGuatemalaAngolaVenezuelaUzbekistanLuxembourgCosta RicaTanzaniaPanamaTurkmenistanCroatiaIvory CoastLithuaniaAzerbaijanGhanaUruguaySerbiaMyanmarSri LankaBelarusSloveniaDR CongoUgandaTunisiaJordanCameroonBoliviaLatviaBahrainParaguayEstoniaNepalLibyaMacauLebanonEl SalvadorHondurasZimbabweCyprusPapua New GuineaSenegalCambodiaIcelandGeorgiaZambiaTrinidad and TobagoBosnia and HerzegovinaHaitiSudanArmeniaGuineaAlbaniaMozambiqueMaliYemenBurkina FasoBotswanaMaltaBeninSyriaGabonPalestineMongoliaJamaicaNicaraguaNigerNorth KoreaGuyanaMoldovaNorth MacedoniaMadagascarBruneiAfghanistanMauritiusCongoLaosRwandaBahamasMalawiKyrgyzstanNamibiaChadTajikistanSomaliaKosovoMauritaniaNew CaledoniaEquatorial GuineaTogoMonacoBermudaMontenegroMaldivesSouth SudanBarbadosFrench PolynesiaCayman IslandsFijiEswatiniLiberiaDjiboutiArubaAndorraSurinameSierra LeoneGreenlandBelizeBurundiCentral African RepublicCuraçaoBhutanCape VerdeSaint LuciaGambiaLesothoEritreaSeychellesZanzibarEast TimorSan MarinoGuinea-BissauAntigua and BarbudaSolomon IslandsSint MaartenBritish Virgin IslandsComorosGrenadaVanuatuTurks and Caicos IslandsSaint Kitts and NevisSaint Vincent and the GrenadinesSamoaDominicaSão Tomé and PríncipeTongaMicronesiaCook IslandsAnguillaMarshall IslandsPalauKiribatiNauruMontserratTuvaluIMF Forecasted GDP by CountryUN RegionGDP (USD Million)
          plotly-logomark
          [59]:
          Selection deleted
          # Save the plot as an HTML file
          fig.write_html('stacked_bars.html')
          html_file = 'stacked_bars.html'

          # Convert the Plotly figure to an HTML string
          plot_html = fig.to_html(full_html=False)

          # Write the HTML string to an HTML file
          with open('stacked_bars.html', 'w') as f:
          f.write(plot_html)
          ## Might delete

          Might delete¶

          [22]:
          # Select GDP source, drop everyrthing, convert to long (unstack converts to tuple)
          y_imf = imf[imf['Forecast'] == page], (axis == 1).unstack()

          # Convert from tuple to array
          y_imf = np.asarray(y_imf)

          # Get the first non zero entry
          y_imf = y_imf[np.min(np.where(y_imf != 0)) : y_imf.size]
          ---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
Cell In[22], line 2
      1 # Select GDP source, drop everyrthing, convert to long (unstack converts to tuple)
----> 2 y_imf = imf[imf['Forecast'] == page], (axis == 1).unstack()
      4 # Convert from tuple to array
      5 y_imf = np.asarray(y_imf)  

NameError: name 'axis' is not defined
          [24]:
          import plotly.express as px
          from jinja2 import Template

          #gdp = px.data.gapminder().query("Region == 'Americas','Asia','Europe','Oceania','Africa'")
          px.bar(gdp, x = "Region", y = "IMF_Forecast", color = "Country", title='IMF Forecasted GDP by Country',
          labels={'IMF_Forecast': 'GDP (USD Million)', 'Region': 'UN Region', 'Country': 'Country'})

          stacked_bars=r"stacked_bars.html"
          input_template_path = r"stacked_bars.html"

          plotly_jinja_data = {"fig":fig.to_html(full_html=False)}
          #consider also defining the include_plotlyjs parameter to point to an external Plotly.js as described above

          with open(stacked_bars, "w", encoding="utf-8") as output_file:
          with open(input_template_path) as template_file:
          j2_template = Template(template_file.read())
          output_file.write(j2_template.render(plotly_jinja_data))

          fig.show()
          /opt/tljh/user/lib/python3.10/site-packages/plotly/express/_core.py:2065: FutureWarning:

When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.
          AmericasAsiaEuropeOceaniaAfrica010M20M30M40M
          CountryUnited StatesChinaGermanyJapanIndiaUnited KingdomFranceItalyBrazilCanadaRussiaMexicoSouth KoreaAustraliaSpainIndonesiaTurkeyNetherlandsSaudi ArabiaSwitzerlandPolandTaiwanBelgiumArgentinaSwedenIrelandNorwayAustriaIsraelThailandUnited Arab EmiratesSingaporeBangladeshPhilippinesVietnamMalaysiaDenmarkEgyptNigeriaHong KongSouth AfricaIranColombiaRomaniaChilePakistanCzech RepublicFinlandIraqPortugalPeruKazakhstanNew ZealandGreeceQatarAlgeriaHungaryUkraineKuwaitEthiopiaMoroccoSlovakiaCubaDominican RepublicEcuadorPuerto RicoKenyaOmanBulgariaGuatemalaAngolaVenezuelaUzbekistanLuxembourgCosta RicaTanzaniaPanamaTurkmenistanCroatiaIvory CoastLithuaniaAzerbaijanGhanaUruguaySerbiaMyanmarSri LankaBelarusSloveniaDR CongoUgandaTunisiaJordanCameroonBoliviaLatviaBahrainParaguayEstoniaNepalLibyaMacauLebanonEl SalvadorHondurasZimbabweCyprusPapua New GuineaSenegalCambodiaIcelandGeorgiaZambiaTrinidad and TobagoBosnia and HerzegovinaHaitiSudanArmeniaGuineaAlbaniaMozambiqueMaliYemenBurkina FasoBotswanaMaltaBeninSyriaGabonPalestineMongoliaJamaicaNicaraguaNigerNorth KoreaGuyanaMoldovaNorth MacedoniaMadagascarBruneiAfghanistanMauritiusCongoLaosRwandaBahamasMalawiKyrgyzstanNamibiaChadTajikistanSomaliaKosovoMauritaniaNew CaledoniaEquatorial GuineaTogoMonacoBermudaMontenegroMaldivesSouth SudanBarbadosFrench PolynesiaCayman IslandsFijiEswatiniLiberiaDjiboutiArubaAndorraSurinameSierra LeoneGreenlandBelizeBurundiCentral African RepublicCuraçaoBhutanCape VerdeSaint LuciaGambiaLesothoEritreaSeychellesZanzibarEast TimorSan MarinoGuinea-BissauAntigua and BarbudaSolomon IslandsSint MaartenBritish Virgin IslandsComorosGrenadaVanuatuTurks and Caicos IslandsSaint Kitts and NevisSaint Vincent and the GrenadinesSamoaDominicaSão Tomé and PríncipeTongaMicronesiaCook IslandsAnguillaMarshall IslandsPalauKiribatiNauruMontserratTuvaluIMF Forecasted GDP by CountryUN RegionGDP (USD Million)
          plotly-logomark
          # Part 2

          Part 2¶

          [29]:
          import pandas as pd
          import plotly.express as px
          import plotly.graph_objects as go
          import urllib, json
          import numpy as np
          [30]:
          # use raw file for csv
          dat = pd.read_csv("https://raw.githubusercontent.com/smart-stats/ds4bio_book/main/book/assetts/kirby21.csv").drop(['Unnamed: 0'], axis = 1)
          dat.head()
          [30]:
          id roi volume
          0 127 Telencephalon_L 531111
          1 127 Telencephalon_R 543404
          2 127 Diencephalon_L 9683
          3 127 Diencephalon_R 9678
          4 127 Mesencephalon 10268
          [41]:
          ## load in the hierarchy information
          url = "https://raw.githubusercontent.com/bcaffo/MRIcloudT1volumetrics/master/inst/extdata/multilevel_lookup_table.txt"
          multilevel_lookup = pd.read_csv(url, sep = "\t").drop(['Level5'], axis = 1)
          multilevel_lookup = multilevel_lookup.rename(columns = {
          "modify" : "roi",
          "modify.1" : "level4",
          "modify.2" : "level3",
          "modify.3" : "level2",
          "modify.4" : "level1"})
          multilevel_lookup = multilevel_lookup[['roi', 'level4', 'level3', 'level2', 'level1']]
          multilevel_lookup.head()
          [41]:
          roi level4 level3 level2 level1
          0 SFG_L SFG_L Frontal_L CerebralCortex_L Telencephalon_L
          1 SFG_R SFG_R Frontal_R CerebralCortex_R Telencephalon_R
          2 SFG_PFC_L SFG_L Frontal_L CerebralCortex_L Telencephalon_L
          3 SFG_PFC_R SFG_R Frontal_R CerebralCortex_R Telencephalon_R
          4 SFG_pole_L SFG_L Frontal_L CerebralCortex_L Telencephalon_L
          [42]:
          ## Load in the subject data
          id = 127
          subjectData = pd.read_csv("https://raw.githubusercontent.com/smart-stats/ds4bio_book/main/book/assetts/kirby21AllLevels.csv")
          subjectData = subjectData.loc[(subjectData.type == 1) & (subjectData.level == 5) & (subjectData.id == id)]
          subjectData = subjectData[['roi', 'volume']]
          ## Merge the subject data with the multilevel data
          subjectData = pd.merge(subjectData, multilevel_lookup, on = "roi")
          subjectData = subjectData.assign(icv = "ICV")
          subjectData = subjectData.assign(comp = subjectData.volume / np.sum(subjectData.volume))
          subjectData.head()
          [42]:
          roi volume level4 level3 level2 level1 icv comp
          0 SFG_L 12926 SFG_L Frontal_L CerebralCortex_L Telencephalon_L ICV 0.009350
          1 SFG_R 10050 SFG_R Frontal_R CerebralCortex_R Telencephalon_R ICV 0.007270
          2 SFG_PFC_L 12783 SFG_L Frontal_L CerebralCortex_L Telencephalon_L ICV 0.009247
          3 SFG_PFC_R 11507 SFG_R Frontal_R CerebralCortex_R Telencephalon_R ICV 0.008324
          4 SFG_pole_L 3078 SFG_L Frontal_L CerebralCortex_L Telencephalon_L ICV 0.002227
          [43]:
          fig = px.sunburst(subjectData, path=['icv', 'level1', 'level2', 'level3', 'level4', 'roi'],
          values='comp', width=800, height=800)
          fig.show()
          ICVTelencephalon_RTelencephalon_LMetencephalonCSFDiencephalon_LDiencephalon_RMesencephalonMyelencephalonCerebralCortex_RWhiteMatter_RCerebralNucli_RCerebralCortex_LWhiteMatter_LCerebralNucli_LMetencephalon_RMetencephalon_LVentricleSulcus_LSulcus_RVentricle Thalamus_LBasalForebrain_LThalamus_RBasalForebrain_RMesencephalon_RMesencephalon_LMyelencephalon_RMyelencephalon_LFrontal_RTemporal_RParietal_ROccipital_RLimbic_RInsula_RAnteriorWM_RPosteriorWM_RInferiorWM_RLimbicWM_RCorpusCallosum_RBasalGang_RLimbic_RFrontal_LTemporal_LParietal_LOccipital_LLimbic_LInsula_LAnteriorWM_LPosteriorWM_LInferiorWM_LLimbicWM_LCorpusCallosum_LBasalGang_LLimbic_LCerebellum_RPons_RCerebellum_LPons_LLateralVentricle_LLateralVentricle_RIV_ventricleIII_ventricleFrontSul_LParietSul_LSylvianFissureExt_LCentralSul_LOcciptSul_LCinguSul_LTempSul_LFrontSul_RParietSul_RSylvianFissureExt_ROcciptSul_RCentralSul_RCinguSul_RTempSul_RLateralVentricle_LLateralVentricle_RThalamus_LBasalForebrain_LThalamus_RBasalForebrain_Rmidbrain_Rmidbrain_LMedulla_RMedulla_LMFG_RSFG_RPrCG_RIFG_ROG_RRG_RMTG_RSTG_RFuG_RITG_RAG_RPoCG_RSMG_RSPG_RPrCu_RMOG_RLG_RCu_RIOG_RSOG_RCingulate_RHippo_RLimbic_RInsula_RPeripheralFrontalWM_Rant_DPWM_RPVA_anterior_RPeripheralParietalWM_RPeripheralOccipitalWM_Rpost_DPWM_RPVA_posterior_RPeripheralTemporalWM_Rinf_DPWM_RPLIC_RALIC_RPVA_posterior_RPeripheralCingulateWM_RCGC_RCGH_RFx/ST_RFx_RSCC_RBCC_RGCC_RPut_RCaud_RGP_RBasalForebrain_RCaudate_tail_RAmyg_RSFG_LMFG_LPrCG_LIFG_LOG_LRG_LMTG_LSTG_LFuG_LITG_LPoCG_LSMG_LSPG_LAG_LPrCu_LMOG_LLG_LCu_LIOG_LSOG_LCingulate_LHippo_LLimbic_LInsula_LPeripheralFrontalWM_Lant_DPWM_LPVA_anterior_LPeripheralParietalWM_LPeripheralOccipitalWM_Lpost_DPWM_LPVA_posterior_LPeripheralTemporalWM_Linf_DPWM_LPLIC_LALIC_LPVA_posterior_LPeripheralCingulateWM_LCGC_LCGH_LFx/ST_LFx_LSCC_LBCC_LGCC_LPut_LCaud_LGP_LBasalForebrain_LCaudate_tail_LAmyg_LCerebellum_RCerebellumWM_RPons_RCerebellum_LCerebellumWM_LPons_LAnteriorLateralVentricle_LPosteriorLateralVentricle_LInferiorLateralVentricle_LAnteriorLateralVentricle_RPosteriorLateralVentricle_RInferiorLateralVentricle_RIV_ventricleIII_ventricleFrontSul_LParietSul_LSylTempSul_LSylFrontSul_LSylParieSul_LCentralSul_LOcciptSul_LCinguSul_LTempSul_LFrontSul_RParietSul_RSylTempSul_RSylFrontSul_RSylParieSul_ROcciptSul_RCentralSul_RCinguSul_RTempSul_RAnteriorLateralVentricle_LAnteriorLateralVentricle_RThalamus_LBasalForebrain_LThalamus_RBasalForebrain_Rmidbrain_Rmidbrain_LMedulla_RMedulla_LMFG_DPFC_RMFG_RSFG_PFC_RSFG_RSFG_pole_RPrCG_RIFG_opercularis_RIFG_orbitalis_RIFG_triangularis_RMFOG_RLFOG_RRG_RMTG_RMTG_R_poleSTG_RSTG_R_poleFuG_RITG_RAG_RPoCG_RSMG_RSPG_RPrCu_RMOG_RLG_RCu_RIOG_RSOG_Rdorsal_ACC_RPCC_Rrostral_ACC_Rsubgenual_ACC_Rsubcallosal_ACC_RHippo_RFimbria_RPHG_RENT_RInsula_RPrCWM_RSFWM_PFC_RSFWM_RMFWM_RMFWM_DPFC_RIFWM_opercularis_RIFWM_triangularis_RRGWM_RIFWM_orbitalis_RMFOWM_RLFOWM_RSFWM_pole_RSCR_RACR_RSFO_RPVWa_RPoCWM_RSLF_RSPWM_RAGWM_RSMWM_RPrCuWM_RMOWM_RLGWM_RCuWM_RSOWM_RIOWM_RPCR_RPVWp_RMTWM_RSTWM_RFuWM_RITWM_RMTWM_R_poleSTWM_R_polePTR_RSS_REC_RRLIC_RIFO_RECCL_RPLIC_RALIC_RPVWl_RPCCWM_RdorsalWM_ACC_RsubgenualWM_ACC_RsubcallosalWM_ACC_RrostralWM_ACC_RCGC_RCGH_RFx/ST_RFx_RSCC_RBCC_RGCC_RPut_RCaud_RGP_RNucAccumbens_RCaudate_tail_RAmyg_RSFG_LSFG_PFC_LSFG_pole_LMFG_DPFC_LMFG_LPrCG_LIFG_orbitalis_LIFG_triangularis_LIFG_opercularis_LMFOG_LLFOG_LRG_LMTG_LMTG_L_poleSTG_LSTG_L_poleFuG_LITG_LPoCG_LSMG_LSPG_LAG_LPrCu_LMOG_LLG_LCu_LIOG_LSOG_Ldorsal_ACC_LPCC_Lrostral_ACC_Lsubgenual_ACC_Lsubcallosal_ACC_LHippo_LFimbria_LPHG_LENT_LInsula_LPrCWM_LSFWM_PFC_LSFWM_LMFWM_LMFWM_DPFC_LIFWM_triangularis_LIFWM_orbitalis_LRGWM_LLFOWM_LMFOWM_LIFWM_opercularis_LSFWM_pole_LSCR_LACR_LSFO_LPVWa_LPoCWM_LSPWM_LSLF_LSMWM_LAGWM_LPrCuWM_LMOWM_LLGWM_LCuWM_LSOWM_LIOWM_LPCR_LPVWp_LMTWM_LSTWM_LITWM_LFuWM_LMTWM_L_poleSTWM_L_polePTR_LSS_LEC_LRLIC_LIFO_LECCL_LPLIC_LALIC_LPVWl_LPCCWM_LdorsalWM_ACC_LrostralWM_ACC_LsubgenualWM_ACC_LsubcallosalWM_ACC_LCGC_LCGH_LFx/ST_LFx_LSCC_LBCC_LGCC_LPut_LCaud_LGP_LNucAccumbens_LCaudate_tail_LAmyg_LCerebellumGM_RCerebellumWM_RMCP_cb_RICP_cb_RMCP_RCST_RML_RSCP_RPCT_RPons_RCerebellumGM_LCerebellumWM_LMCP_cb_LICP_cb_LMCP_LCST_LPCT_LSCP_LML_LPons_LLV_body_LLV_Frontal_LLV_atrium_LLV_Occipital_LLV_Inferior_LLV_body_RLV_Frontal_RLV_atrium_RLV_Occipital_RLV_Inferior_RIV_ventricleIII_ventricleFrontSul_LParietSul_LSylTempSul_LSylFrontSul_LSylParieSul_LCentralSul_LOcciptSul_LCinguSul_LTempSul_LFrontSul_RParietSul_RSylTempSul_RSylFrontSul_RSylParieSul_ROcciptSul_RCentralSul_RCinguSul_RTempSul_RChroid_LVetc_LChroid_LVetc_RThalamus_LBasalForebrain_LHypoThalamus_LCl_LMammillary_LThalamus_RBasalForebrain_RHypoThalamus_RCl_RMammillary_RMidbrain_RCP_RRedNc_RSnigra_RMidbrain_LCP_LSnigra_LRedNc_LMedulla_RICP_RMedulla_LICP_L
          plotly-logomark
          [44]:
          # Create Sankey diagram
          fig = go.Figure(data=[go.Sankey(
          node=dict(
          pad=15,
          thickness=20,
          line=dict(color="black", width=0.5),
          label=['ICV', 'Telencephalon_L', 'Telencephalon_R', 'CerebralCortex_L', 'CerebralCortex_R', 'Frontal_L', 'Frontal_R', 'SFG_L', 'SFG_R', 'SFG_PFC_L', 'SFG_PFC_R', 'SFG_pole_L'],
          ),
          link=dict(
          source=[0, 0, 1, 1, 2, 2, 3, 3, 4, 4], # indices correspond to labels
          target=[1, 2, 3, 4, 5, 6, 7, 8, 9, 10],
          value=subjectData['comp'].tolist()
          ))])
          [45]:
          # Update layout
          fig.update_layout(title_text="Subject 127 Data as Sankey Diagram", font_size=10)

          # Show plot
          fig.show()
          ICVTelencephalon_LTelencephalon_RCerebralCortex_LCerebralCortex_RFrontal_LFrontal_RSFG_LSFG_RSFG_PFC_LSFG_PFC_R
          Subject 127 Data as Sankey Diagram
          plotly-logomark
          [60]:
          # Save the plot as an HTML file
          fig.write_html('sankey.html')
          html_file = 'sankey.html'

          # Convert the Plotly figure to an HTML string
          plot_html = fig.to_html(full_html=False)

          # Write the HTML string to an HTML file
          with open('sankey.html', 'w') as f:
          f.write(plot_html)
          # Links to Webpage

          ## Stacked Bars


          ## Sankey Diagram

          Links to Webpage¶

          Stacked Bars¶

          Sankey Diagram¶

          No properties to inspect.

          Kernel usage not available

          Switch to a notebook or console to see kernel usage details.

          -

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              ipythongfm
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              1
              sankey.html
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